My name is Keegan.
Got into the cannabis space in 2018 at a laboratory and have now been working with cannabis data
going on for plus years now.
So love to hear about what you'd like in the industry, what you're doing and what you hope
to get out of the group.
So Daniel, would you mind kicking it off?
Sure.
I just completed a data analytics program over the past few months and was looking at
different types of data events and this popped up at the top.
So I figured I'd come check it out.
Certainly.
My name is Dean Pangelinan.
I'm a senior business intelligence strategist for a company called Merge World.
We're in the digital marketing space and some of our clients are actually in the cannabis
industry.
So I'm wanting to learn more about the scientific analysis of cannabis data because I am a statistician
by training and work primarily in the visualization and representation of the data.
So I want to be able to find out how I can best help our clients and candidates.
You're definitely in the right place.
Data visualization is a key part of what we do.
So would love to have your expertise.
I'm thrilled to pick your brain.
TJ, would you be interested in introducing yourself?
Sure.
Hi, I'm TJ Goff.
I'm a back end engineer working at Lantern, which is a cannabis delivery startup based
out of Boston.
Too cool.
Cannabis delivery is a big, big thing.
So you're well positioned for a profitable future.
So awesome.
That's the idea.
We'll have to pick your brain on transportation because we've got a lot of transportation
data.
Ramni, would you be interested in introducing yourself?
Yes.
Hi, thank you for organizing this meetup.
So I'm taking this program in data analytics and I'm learning all the machine learning
and statistical modeling and data visualization and all the different techniques.
So when I saw this, I found the data is interesting, like cannabis data.
Wow.
I've never seen that data.
So hence I'm joining and let's see what I learned from here.
Thank you.
Awesome.
You're also in the right place.
Thanks for joining.
Trav, would you be interested in introducing yourself?
Yes, indeed.
I'm Trav.
I'm just interested in the industry and I want to learn more about data analytics.
Too cool.
It's going to be a big field.
So once again, there's a high demand for people with data analytics skills.
So you're well positioned too.
Well, without further ado, I'll go ahead and share with you some of the work I've been
doing because really it's just to guide a discussion.
So don't let me steal the show, but hopefully it'll spark a good conversation here.
Well, welcome to the meetup.
Last week we looked at laboratories.
And then this week I figured let's get a bit more to the production side.
We'll get to cultivation next week, but we can start with processing.
So I'm sure all of you are acquainted with processed cannabis.
Up in the top left corner, we've got product known as Shatter testing at 74% THC.
And so this is a new and an old process.
So I was going to dive in a bit to the history before we get to the data because it's so
fascinating to know where all these processes came from.
So I said, you know, humans are basically just remarkably good at using plants as factories
to produce complex chemicals.
And people have been doing this with cannabis hemp for a long time now.
So people think that cannabis was being processed in India as early as 1000 BC.
So we're going on potentially 3,000 years of people processing and using cannabis.
And they may not have had the high THC that we had today, so that may be why it was important
to produce concentrates.
So just fascinating history here.
And we'll dive into some of the more modern history.
So what we all know now as cannabis brownies, and sorry you got cut off here, we've got
Alice B. Topeless, and I'll send out a reformatted presentation.
But she was the first one to really write a cookbook with cannabis in chocolate.
So you know, you can consider that processing.
And the recipe was by Brian Dyson, who was a restaurateur.
So interesting history there.
So they were cooking with cannabis, concentrating it.
And then beginning in 1940 and really accelerating into the 1960s is when people really started
isolating the various cannabinoids.
So you know, when you look at the grand scheme of things, we've only been, you know, concentrating
these products and really, you know, analyzing them on a compound by compound basis for 60
or so years, maybe 80, but about 60, you know, rigorously, scientifically.
So that's pretty exciting that, you know, cannabis has been around, you know, 3000 years
or so.
And now we get to really get scientific at a real molecular level.
So it's a real exciting, real exciting work, you know, still to be done.
I'll kind of skim over this, but you know, these days just wanted to talk about how cannabis
extraction is done.
A lot of times it's done with organic solvents.
You can do solventless, which is anecdotally becoming more popular.
So some of the solvents that you may use are hydrocarbons.
You also can use butane and carbon dioxide.
But you know, you've got to be careful, right, because not only are these chemicals flammable,
but you don't necessarily want to be ingesting them either.
So there's, you know, a purging process where, you know, you try to get rid of all these
solvents.
And then, you know, we see the data at the laboratory where not only are you testing
for CBD, THC, you also want to make sure you don't have these residual solvents left in
your product.
And in particular, what we'll be talking about today is processing variability.
So we'll be looking at the various compounds that producers may be trying to isolate.
And we'll look at essentially how good they are at doing that.
So that was our original question is, you know, how to run a profitable processor.
And one can argue that focusing on variability could be key.
So for financial reasons, you're always trying to minimize risk.
And in finance, you often just use risk as a synonym for, you know, variability, you
know, your standard deviations in returns.
So from a financial point of view, which drives a lot of the action here in the cannabis industry.
So also for economic reasons, people are risk averse.
So people are just predisposed to processes that aren't unpredictable.
From an engineering point of view, you want to minimize your variance because it's going
to be easier to design your system.
It's going to be much harder to design your process if you've got variation in your extraction
techniques.
And then, of course, from a business marketing and public health perspective, you know, you
want to maximize your consistency of your product.
So marketers are anecdotally talking about how they want a consistent product for their
customers.
And then from a public health point of view, if people are using these concentrates for
medicinal purposes or what have you, you know, you want to make sure that people are, you
know, consuming what they think they're going to be consuming.
So that's where, you know, consistency, you know, matters from, you know, a public health
point of view.
And for the newcomers, I'll let you introduce yourselves here in just a minute.
I'll talk about the data in the code here, and then we can reintroduce ourselves for
the newcomers and then just have open discussions.
And please interrupt me if I'm, you know, moving a little fast here.
Awesome.
Well, on to the data.
So always working with open source data here.
And I'll share these links with you afterwards.
But the Washington state has a very liberal Freedom of Information Act.
And so entities like the Cannabis Observer will do periodic Freedom of Information Act
requests to get access to this publicly available data.
And so here is a release from November of 2021.
So still quite recent data.
And this data spans from 2018 till November of 2021.
Caveat.
This data can be notoriously hard to work with.
So for example, we're looking at the lab results and, you know, unzipped, you know, the lab
results can be quite sizable.
So I've downloaded the these data sets here.
We have clocking in at 31 gigabytes.
We have the inventories data weighing in at 14 gigabytes.
We've got the inventory types data.
And then we have the various lab results data, which is about two and a half gigabytes total.
So we have almost 50 gigabytes of data here just pertaining to lab results.
So that can be a daunting task to get your mind around.
But we've written a useful script here, which I'll actually go ahead and share with the
group here.
Probably should have committed this earlier.
But here's the latest code for today's meetup, which you can find on the cannabis data science
GitHub repository.
So for those of you who are new, you can find all the source code here in the cannabis
data science repository.
So that way you can follow along from the code for today.
And so today the code was quite long.
So I'm not going to necessarily go through it line by line.
But if you need Python snippets for how to parse data, then this is a useful script.
But basically what I do is I read in all of these lab results.
And just to give you an idea of some of the data points we're working with here.
For each lab result, we have a handful of cannabinoids, solvents, and then of course,
some of your other screenings, such as for foreign matter, microbes, moisture, and microtoxin,
as well as some other IDs.
We can then match this with the licensees data.
So we can get fields such as the license number.
We can even get things like the city and address.
So zip code of where the licensee may be.
So that way you can even do geographic analysis.
Also have a rich inventories fields.
So here we can get just a couple things of interest.
So in a future work, I want to look at quantities.
So the quantities being produced.
Of course, we're looking at inventory types.
And this is where we get a lot of our IDs.
Then we have to match this with the inventory type to find out what type of product it is.
And then finally, we can get the name of the strain that people gave their product.
So this script essentially does that.
So reads in your lab data, combines it with inventory data, combines it with inventory
type data, combines the lab results with strain data.
Then you combine the lab results with licensee data.
And then you get all of the lab result fields that we're interested in here.
So here are all of our fields we've collected to this point.
A lot of identification fields.
And then we add on all the cannabinoids and solvents and everything else that we would
like.
And I'll let you play around with this script.
But the exciting thing is the data.
So here we started with almost 50 gigabytes of data.
On my first pass, I just shaved it down to the bare minimum with 30 megabytes of IDs
and two gigabytes of names.
And then you can start to clean that out.
So if you add the IDs with the licensee data, actually first you add it with the strain
name data.
And then you're around 40 megabytes.
You add that with the licensees data and you're around 90 megabytes.
And then you add back the lab results data.
And you're sitting less than 200 megabytes, which is something to be happy with when you're
starting out with 50 gigabytes of data.
So that just shows you how much cleaning can reduce the fingerprint of your data.
So here we just stripped out all the extraneous fields and we removed all the null values.
And what we're left with is a large data set, but less of a size than some apps that are
out there.
So it's still large, but it's something that we can work with here.
So just to give you a bit of a tutorial of the data, you've got your IDs.
You know what lab tested the cannabis.
You've got the name that people attach to the inventory.
As well as we now know what strain it is.
And so you could do a incredible analysis of strains.
So people have been really curious about what's in a strain, what's in Pangea, what's in train
rack.
So you can do an analysis here and try to find out what strains are people growing and
which ones may look the most profitable.
So that's a whole nother can of worms.
But you can add a geographic layer.
So maybe people are growing certain strains in certain parts of the state.
So certain strains may be better in certain climates.
You know what type of product it is.
And so today we'll be specifically looking at concentrates.
And then of course the gold, you have all of the cannabinoid data.
So it would be awesome to have terpene data too.
But we'll take what we're given.
And in this case, we are given an awesome data set of cannabinoids.
And you can also utilize data on residual solvents, microbes, mycotoxins.
You know, just so that you can maybe see what other factors play into this, right?
Because you also want to be growing strains that are, you know, handle stressors in your
environment.
So perhaps certain strains may be more likely to fail for micro or myco.
I don't know.
I'm just kind of conjecturing.
But there could be many interesting aspects in the data.
So there you have it.
We've taken a 50 megabyte data set and flattened it down into 200 megabytes.
So without further ado, I'll go ahead and jump into a bit of exploratory analysis.
Are there any questions so far?
Just checking real quick.
I'm still with you all.
All right.
We'll must have you all entertained.
So we'll get into some exploratory data analysis here, right?
So we've got an awesome data set here.
What's said and done, you know, we're looking at between 100,000 to, you know, 420,000 or
so, 418,000.
So observations, a lot of these may have missing values.
And different people, so here's a good example.
Different people have different naming conventions.
So for example, this cultivator is really good about putting their strain name in, but
they don't have any informative inventory name.
Whereas other producers may have informative inventory names such as this producer.
However, they've got less informative.
Well, it's a give or take.
So long story short, you need to look at both of these inventory names and strain names.
But that's a whole other can of worms.
We're going to get into cultivation next week.
And this is definitely going to be something we look at.
Because you know, the strain you grow, that's a critical choice.
So we've got awesome lab result data there.
And I'll make this available for the meetup group since we're all about open source, open
data here.
Cool.
Well, let's get off to the races.
So we don't need many tools for today.
Probably just some of the basic Python functions.
You know, well, just to recap, I was just saying, we're just going to start with some of these
core packages, Matplotlib, pandas.
You can go a long way with them.
And I was just going to say, you can think of Seaborn as an extension of Matplotlib.
So for me, it's easy to conceptualize it that way.
That way, you just use Seaborn where Matplotlib falls short.
But you know, that's sort of the nitty gritty.
I use it for things like colors.
And then quick tip is, if you just want nice looking plots, just throw everything in 538
style and things look nice right out of the box.
So I already have the data read in here.
And here is a snippet that you'll be able to use once I share this data with you, where
you can pick and choose which variables you want.
So for example, if you don't need these variables, you can just comment them out and then read
in the data that you do need.
For example, the data I've read in, if you look at an observation here, we've got a big
rich JSON data set here.
So we have all the variables that I have already showed you in a nice JSON format.
So awesome.
Awesome, awesome, awesome.
Well let's get to some of the interesting bits.
So I realize now that we're also going to need to include, to say we're looking at processed
products.
You know, we'll also need to include products where they may have used solventless extractions.
So let's see if we can't identify some of these lab results where they use solventless.
So the way we can do this is first we can remember, let's look at some of these fields
that we have here.
So we've got inventory name, not quite what we need.
We need the inventory type.
I think we may even want sample.
Here we are, intermediate type.
Let's just list out here all of the different sample types that we have.
So here they are.
Today not going to be looking at flour.
So next week we'll look exclusively at flour.
For today, just kind of wanted to do a deep dive into concentrates.
That way we're just comparing apples to apples.
So let's see if we can't just say, okay, let's get everything where they either did a solvent
test, or we've got these intermediate types that doesn't require
solvent screening.
So I believe if you're using a food grade solvent, then you don't need to get residual
solvent screening.
It should be included in the or statement nonetheless.
And then of course you don't need residual solvent screening if you're using a non solventless
extraction method.
So I may have missed some.
So this may exclude products such as edibles.
We may want to repeat this analysis specifically for edibles to look at the average concentration
in edibles.
However, just going to say, I don't believe there's as much variability in edibles due
to the regulations on edibles.
I do believe there's a limit on the milligrams of THC and CBD that can be in an individual
edible.
So you see, for example, if the limit is just picking a number here, 10 milligrams per gram,
then you're just going to see a lot of people trying to produce exactly 10.
So it's worth a deep dive.
And they just going to try to work here on processed products.
So we basically are just going to start doing a bunch of conditional averages.
I say you can go so far with that.
It's extraordinary.
It's almost like you just kind of keep layering on conditions and the data just becomes more
and more interesting.
So let's just once again, let's just look at our intermediate types here just to ensure
that we didn't get anything odd in the mix.
And we did.
We've got some flowers and some products we couldn't identify here.
So let's just see if we can't just exclude those real quick.
So let's just say we want all the products where we don't have, let's just say everything
where the intermediate type is there.
We don't want to look at flowers for today.
That's probably just a miscoding there.
So let's see if we can refine our products a little better.
OK, so we can't negate that.
So we'll negate there.
That's better.
Awesome.
So let's just double check that we just have.
Well, somehow it looks like you may just have to proceed as is.
That's for expediency's sake here.
OK, so not getting those.
Aha, just need to fix this to an and condition.
We may not be able to get rid of the nans for now, but that's OK.
We're able to at least exclude the flowers.
Question?
OK, OK, Dean, apologize though.
It is a bit, but good to have you nonetheless.
And sorry for all that.
It may be a little scattered today.
I spent most of my time just writing this big script to compile all the data.
So next week, I'll try to have things a bit more organized.
So that's why it's sort of just been from the top today.
So my apologies, but we'll hone things in through the future.
But without further ado, let's get to some interesting numbers and some figures here.
We should have gotten to the figures before Dean had to go.
So we'll go ahead and get the total cannabinoids here.
Interesting.
It looks like we may have missed some of these cannabinoids.
Interesting.
So it looks like we may have missed some of these cannabinoids.
So I think I may have missed CBN.
You've missed some of these.
Interesting.
Oh, well, we'll proceed with the data that we do have.
OK, also don't have CBGA and THCV.
So my apologies.
I'll have to add those cannabinoids back to the data set.
Awesome.
So we now are down to a small data set here, only have about 800 observations.
So want to relook at why that may be.
So like I said, this is sort of the first go at analyzing this data.
The different types of concentrates you see in the market.
So this is where things actually get interesting.
So we can see, wow, a bulk of the market is done with ethanol concentrates, followed by
hydrocarbon, followed by CO2, with food grade and non-solvent lists making up just above
10% of the whole market.
So any thoughts or comments about this breakdown?
Question?
Yeah, for the solvent versus non-solvent base, would the non-solvent be a lot more expensive?
I'm not too familiar with the processes.
Is that why it would be lower?
At least one of the reasons.
Well, you raise a real interesting point.
And so this is something that depends.
So at the retail level, do consumers have a demand for non-solvent list extracts?
So that's an awesome question.
So we should combine this with retail data and see, does two products that are made with
solvent list methods, do they retail at a higher price than other concentrates?
So is there a premium place on non-solvent list?
And it also may depend on the concentration.
So I've been wanting to do an analysis on, does concentration of cannabinoids affect
the retail price?
I don't know if anybody's looked at that yet.
And I think it would be so easy to do with this data that we have here in Washington.
All we would do is run a regression of price, which we have.
We're at sales.
We have sales on the concentration of that product and see if there's a positive or negative
correlation.
All we're going to do is add a time component to our analysis.
And then we're essentially going to track the breakdown of analyses used over time.
So is solvent list becoming a greater or a smaller proportion of the market?
We don't know, but we can easily answer that.
Did that answer your question?
Yeah, for the most part.
I'm wondering about price and also if it's a lot more difficult to do, which I'm assuming
it would be.
Exactly.
If you're doing a high pressure non-solvent list extraction, that may be challenging.
There are people out there who are doing it.
And then yes, the price point is critical.
So let's look at it.
Let's essentially combine this data with the price data.
And like I said, it would just be interesting to see if there's any correlation between
price and any of the other factors.
Because it doesn't necessarily matter.
It all depends on consumers' preferences.
Too bad one of our Classic members isn't here today because she could talk about her preferences
because consumers are hard to predict.
So it's really hard to predict what they'd like.
But we can look at the data.
Awesome.
Just to round it out here, we can start looking at some of the concentrations.
So this is just sort of pseudo code.
So still need to make sure that everything works correctly.
But here we have the average concentration and the standard deviation found in various
concentrates.
So let's just look at the average concentration first.
So here you have, topping the charts, this actually kind of surprises me.
Topping the charts, you have ethanol coming in at an average of 81% total cannabinoids
with hydrocarbon concentrates just behind at an average of 80%.
And then quite a lower average, you've got CO2 concentrates and food grade solvents around
60-61% for CO2.
I find that interesting.
And then here you see the non-solvent list actually has the lowest average cannabinoids.
And I would conjecture that that's because I wouldn't be surprised if people who are
doing non-solvent list extracts may be doing things like bubble hash, which I don't think
necessarily will have the extraction.
I'm not sure what the word is for it, efficiency perhaps, that some of the other extraction
may-
No, that makes sense.
Do you think more product would have to be used because it's less efficient and that
might be part of the high enterprise, I guess?
Well, that's what I have in my prior is that it does.
So my prior is that consumers have a preference for higher cannabinoid concentrations.
So you'd get a price premium, right?
They're basically paying per milligram of THC.
So you'd have to sell something that's 53% at a lower price than something that's 80%
THC for the consumer to purchase the same milligrams of THC.
So if you abstracted that, oh, the consumer's in the market for milligrams of THC, then
you would expect that something with 53% would have a lower price point.
If it's got a lower price point, there may not be as many people in the market for it.
But I think this is critical to look at the strain name on this slide because I think
the strain and inventory name could be really informative because you may want to try to
separate out if there's different types of these non-solventless extracts.
So for example, some may specifically say there's bubble hatch and others may not.
And it would be interesting to see if there's any groupings that you can make within these
product categories.
And then basically, just to finish out what we began with, essentially I was saying, OK,
you've got your different extraction mechanisms and they're going to have their different
efficacy rates.
If you have two extraction mechanisms that are about the same, so that would be hydrocarbon
concentrates and ethanol concentrates, and as well CO2 concentrates and food grade solvent
concentrates, then from these reasons that we stated in the presentation, from financial
reasons, economic reasons, engineering, business marketing, public health, you would gravitate
towards the one that has a lower variance, lower standard deviation.
So here, let's see if we can't pick the ones that would win.
So if we're comparing hydrocarbons to ethanol extraction, ethanol just takes the cake.
So hydrocarbon concentrates, and like I said, we want to look at this like a processor by
processor basis and even see if we can't disentangle these into more subgroupings.
But right out of the bat, our first impression is, oh, hydrocarbon concentrates have standard
deviation of almost 10%, whereas ethanol concentrates by and far have the lowest variability, the
lowest standard deviation, but their standard deviation being less than 4%.
So that means you would expect around 95% of your products would be within 8% of 81%.
So you would expect your products, they could be varying from around 73 to around 90% concentrate.
And this is why you could also look at it by a processor by processor basis, which is
exactly what we'll be doing on Saturday, because we can do this exact same analysis processor
by processor and over time.
And what that would show would be, okay, are the processors who are testing the most samples,
do they have a high concentration and do they have a low standard deviation in their concentration?
And I would naively predict that there's a positive correlation between average concentration
and the total number of samples you test, as well as a negative correlation between
your standard deviation in the samples you test.
And so I would just basically say the people who are able to operate with the lower variability,
I conjecture they're going to be more profitable.
We'll let the data show on Saturday.
And also, you know, we can look at this over time to see, okay, are people honing in their
processes over time?
Are people's standard deviations, is this decreasing over time?
Is it increasing?
How about concentration?
Is concentration increasing, decreasing, staying constant?
Is for example, if concentration starts to become pretty stable, everybody sort of, they've
got their extraction techniques.
That may not be the case.
People may be getting better and better extraction techniques over time.
Have a standard deviation of almost 18%.
I was just going to say rounding out for today, if you were going to compare food grade solvents
with CO2 concentrates, you would look at the standard deviation since their average concentration
is about the same.
And you would see people with who are producing with food grade solvent concentrates on average
have a standard deviation of almost 18%, which is when you think about it, quite extraordinary.
I would suggest, you know, it wouldn't be implausible to have your food grade concentrate,
you know, almost 100% as well as almost 10% or 15%.
So would that actually shake out?
May or may not.
But long story short, that's quite a, that's quite a high, quite a high variation.
Same with the non solvent list, the people who are doing non solvent lists, big variation.
So if, you know, if I were to suggest an extraction method, you know, well, I may not suggest
this one.
So, you know, so if you were going to do food grade solvent versus CO2 concentrate, you
know, it does look like, you know, CO2 concentrates have about the same, if not higher average
concentration.
And they also have a lower standard deviation.
Keep in mind, it matters what you measure.
So remember here, we still have a whole lot of other factors to look at.
So in particular, the solvents.
So I would want to know, okay, our CO2, it's got it around the same concentration, it's
got lower standard deviation.
What's its failure rate?
Because you know, if we're failing for solvents, left and right, that's not going to necessarily
be profitable.
So this is why, you know, running a profitable business, it just matters on so many metrics,
on so many fronts, right, you've got to worry about your concentration, you've got to worry
about your standard deviation, you've got to worry about your failure rate.
You know, and for example, what are consumers preferences?
So that's why it'd be interesting to see what the shakedown is in the market over time.
So are consumers gravitating towards one type of product or another?
You know, it's just just so many factors here that play out in running a profitable producer,
processor.
And I think the lesson of the day is, you know, variability matters.
So you know, just if you're looking at variability, you can start to make educated decisions of,
okay, do I choose hydrocarbon concentrate, do I choose ethanol?
And like I said, this isn't the end of the story.
You'll want to see if you can't make subgroups.
So see, okay, can we use the strain name and inventory name to even further classify these
samples?
And then also, of course, you'll want to, you know, take a look at some of the variables
that you're screening for just to make sure that, you know, everything's passing at the
rate that you want it to.
And for that, I'm going to go ahead and conclude the presentation just so I'm respectful of
everybody's time.
However, were there any questions?
I know it was a bit of a rocky start, but hopefully we got into gear there towards the
end.
So any questions?
Comment?
I guess just for the forecasting you're talking about on Saturday, you guys usually just run
like simple regressions, pretty much.
We heavily lean on Fox Jenkins slash Arima forecasting, if you may have heard of it.
So it's essentially a time series technique.
You just use an observation that you can track over time.
In this case, we'll just be tracking concentrations over time month by month.
You could potentially do day by day.
I like month by month or even week by week, week by week is quite powerful.
And then you just use a series of observations and you forecast them into the future.
So you basically use things like, oh, does this series have a trend?
Is there a moving average component?
And then you basically, it is a regression model.
It is a regression model at the end of the day.
And yes, you can get quite accurate, surprisingly accurate forecasts for the future.
So I always like to say it's a heck of a lot better having a guesstimate of the future
than no estimate of the future.
So definitely join us if you want, because not only will we be forecasting the average
concentration for aggregate and by individual processor, we'll also get to forecast their
variability, which is a more advanced statistical technique, but we'll do it simply and easily
because that's the way things should be done.
And we can get a nice forecast for people's variability.
And that way we can rank order all of the processors and see, OK, who's got the highest
average concentration week by week?
Who's got the lowest standard deviation week by week?
And then see how the industry is moving.
So is there any trend in average concentrations?
Are people getting more efficient over time?
We'll be able to answer all those questions.
It will be exciting.
OK, thank you.
Definitely.
And if you can't make it on Saturday, just feel free to register.
It's $1 and I'll send you all the source code and material.
So I hope your value will be tenfold, if not more.
So check it out, Saturday morning statistics.
If not, then next Wednesday we'll pick up with producer cultivators.
So we'll look more at the cultivation side next time, maybe kind of recap on what we
did today.
So it's extraordinary work.
So if you have any breakthrough discoveries, questions along the way, ideas, feel free
to share them because it would be awesome to have a collaborative effort to really dig
deep into this data because there is so many questions to answer in not enough time.
We'll do it.
We'll do a lot though.
Thank you all for coming.
So I can't thank you enough for coming.
So it's been fun.
And hopefully we were able to get through some of the internet lags there and maybe
you found it worthwhile.
So definitely feel free to share some of your feedback.
I'm always here if you want to send a message.
Awesome.
Thank you, Deacon.
Awesome.
Thank you all.
Let's get in stone and have an awesome week!